Urban surface water bodies mapping using the automatic k-means based approach and sentinel-2 imagery

Abstract

Rivers, lakes, and open water bodies play crucial roles in environmental development, especially in urban ecosystems. Accurate urban surface water body maps in high resolution are an important prerequisite for better and faster decision making for urban ecosystem monitoring, mitigating the effects of urban heat islands and urban climate change adaptation. Research presents new automatic algorithm for urban surface bodies mapping (AUWM). Algorithm was tested on Sentinel-2 data and can be applied globally for automatic mapping water bodies in 10-m spatial resolution. AUWM was developed based on modified normalized difference water index, pansharpening techniques (MNDWI_PS), and k-means clustering algorithm. Research was provided on three study sites. The optimal number of classes for k-means in AUWM is four. Accuracy assessment results show that AUWM is a highly accurate method for water bodies mapping, confirmed by all statistical parameters; accuracy, kappa, precision, and F1 value are 0.997, 0.830, 0.998, and 0.998, respectively.

Keywords:

• Unsupervised classification
• k-means
• Sentinel-2
• urban water bodies
• automatic mapping

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors would like to thank the University of Zagreb that funded the RS4ENVIRO project entitled: ‘Advanced photogrammetry and remote sensing methods for environmental change monitoring’ (Grant No. RS4ENVIRO) and European Space Agency that funded the RS4ENVIRO project entitled: ‘Automatic monitoring of narrow-leaved ash (Fraxinus angustifolia Vahl) forests by remote sensing methods and Copernicus data’ (Grant No. RS4EST) under which this research was conducted. The authors would like to thank the European Space Agency for providing the Sentinel data free of charge.